SEDLAC is a database that includes statistics on poverty and other distributional and social variables from 25 Latin American and Caribbean (LAC) countries. All statistics are computed from microdata of the main household surveys carried out in these countries using a homogenous methodology (data permitting). Statistics are updated periodically.

SEDLAC allows users to monitor the trends in poverty and other distributional and social indicators in the region. The dataset is available in the form of brief reports, charts and electronic Excel tables with information for each country/year. In addition, the website visitor can carry out dynamic searches online.

SEDLAC is an ongoing project. All statistics shown in this site are preliminary, since all statistics are permanently updated and revised. We are grateful to all comments and suggestions that help improving the database.

Information taken from this database should be cited as "Source: SEDLAC (CEDLAS and The World Bank)" or "Source: Socio-Economic Database for Latin America and the Caribbean (CEDLAS and The World Bank)". We advise making reference to the date when the database was consulted, as statistics are periodicaly updated.

The SEDLAC database is divided into 12 sections: household surveys, income, poverty, inequality, demographics, education, employment, housing, infrastructure, durables goods and services, aggregate welfare and pro-poor growth.

Each section contains at least an Excel file with multiple sheets. Each of these sheets shows a data table with specific information for each of the 25 Latin American and the Caribbean countries (or those in which data is available).

Household surveys are not uniform among countries of Latin America and the Caribbean. In particular, they differ significantly in their geographic coverage and questionnaires. There are also differences in the periodicity of surveys within a country.

This project seeks to ensure that statistics are comparable, as far as possible, between countries and over time. This is done using similar definitions of variables in each country/year and applying consistent methods of data processing. However, it is impossible to ensure a perfect comparability. 

This webpage presents sufficient documentation to allow each user to decide whether or not to compare, considering the available information, their preferences and needs.

SEDLAC database includes information from over 200 household surveys carried out in 25 LAC countries: Argentina, Bahamas, Belize, Bolivia, Brazil, Colombia, Costa Rica, Chile, Dominica, Dominican Republic, Ecuador, El Salvador, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Suriname, Uruguay and Venezuela.

In each period the sample of countries represents more than 97% of LAC total population. The database mainly covers the 1990s and 2000s, although we also present information for previous decades in a few countries.

Even though we recognize that household consumption is often a better measure of welfare than household income, a practical reason justifies the use of family income as the welfare proxy in this project: few countries in Latin America and the Caribbean implement routinely households surveys with consumption or expenditure questionnaires, while all countries include questions on individual and family income.

While most countries have expenditure surveys, they are usually carried out with long intervals of time (in many cases, every 10 years), so they are not suitable for monitoring poverty, inequality and other relevant social indicators.

We construct individual income by adding all income sources. Whenever possible we distinguish among income from salaried work, self-employment and salaries assigned to owners. Whenever possible we compute labor income from the main activity. Individual non-labor income is divided into three categories: (i) pensions; (ii) capital and benefits; and (iii) transfers. Countries differ in the questions devoted to capturing capital income, interests, profits, rents and dividends. For comparison purposes. we prefer to gather all these questions in a single category. The same criterion applies to transfers, although we also construct a variable that, whenever possible, identifies those transfers made by the government, and other that captures transfers clearly associated to poverty-alleviation programs (e.g. transfers from Programa Jefes de Hogar in Argentina). 

Since we are interested in capturing current income, non-current items are not included in our definition of income. The same criterion leads to the exclusion of income from the sale of some goods and assets like vehicles, houses, or stocks. We also exclude income from gifts, life insurance, gambling and inheritances.

Once we have individual income, we construct household income by adding income for all members from the household. Household per capita income is computed as the ratio between total household income and the number of members. Finally, we compute household income adjusted by several equivalence scales (see below for a particular FAQ on this issue).

Yes, it is included. The concept of income considered in SEDLAC refers to the flow of resources obtained as remuneration to the use of all the assets owned by an individual or household. According to this definition, income should include not only the returns for the use of labor and capital, but also any other rents produced by the possession of durable goods, such as houses or cars.

Families living in their own dwellings implicitly receive a flow of income equivalent to the market value of the service that the use of this property represents for them. This remuneration should be computed as part of household income, even though it is never recorded in a formal market.

In some surveys owners are asked to estimate the rent they would have to pay if they had to rent the houses they occupy. The answers to this question are used to impute rents to own-housing, although issues of reliability in the answers are usually raised, in particular in areas where housing markets are not well developed.

In those surveys where this information is not available or is clearly unreliable we increase household income of housing owners by 10%, a value that is consistent with estimates of implicit rents in the region.

It is common in household surveys some of the individuals interviewed refuse to answer certain questions. In particular, questions related to income are those that tend to have higher percentages of non-response.

If the decision not to answer questions related to income depends on the income level of individuals (for example, if the probability of non-response is higher for the rich), this non-response could generate a bias in the estimated statistics.

There are several methodological options to address this problem. The most used is the imputation of income to individuals who do not answer the income questions. This can be done by using matching techniques or applying the estimated coefficients of a Mincer equation.

However, these methodological choices are not free of problems. Several decisions must be taken to implement the adjustment. The researcher should choose an estimation procedure, pick the dependent and independent variables, select a method for imputing error terms, and so on. Even when all the steps are clearly documented, people may be suspicious on the way the data is treated and on the choice of a particular imputation strategy. Working with the raw data has the advantage of more transparency.

Due to these reasons, in this version of the SEDLAC we compute the statistics with the official datasets, as it has been done in most academic and official studies. Later implement a proper procedure for the allocation of income.

Suppose income from source s is missing for individual i. Should we record as missing that individual’s total income? If we take that alternative, should we in turn record as missing the total income of individual i´s household? We make the following (necessarily arbitrary) decisions:

If s is not the main source of income for i, then we compute the individual total income ignoring source s.

If instead s is the main source, we record total income as missing.

This alternative has the advantage of not dropping from the datasets individuals who do not respond questions on income sources of secondary importance. The cost to be paid is the income under-estimation for these individuals.

Regarding household income, we record it as missing if the household head’s total income is missing. Otherwise, we compute household income assigning zero income to non-heads with missing income.